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CertNexus Exam AIP-210 Topic 1 Question 40 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 40
Topic #: 1
[All AIP-210 Questions]

You have a dataset with many features that you are using to classify a dependent variable. Because the sample size is small, you are worried about overfitting. Which algorithm is ideal to prevent overfitting?

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Suggested Answer: B

Performance testing is a type of testing that should be performed at the production level before deploying a newly retrained model. Performance testing measures how well the model meets the non-functional requirements, such as speed, scalability, reliability, availability, and resource consumption. Performance testing can help identify any bottlenecks or issues that may affect the user experience or satisfaction with the model. Reference: [Performance Testing Tutorial: What is, Types, Metrics & Example], [Performance Testing for Machine Learning Systems | by David Talby | Towards Data Science]


by Johana at Mar 20, 2025, 11:57 AM

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Yolande
12 days ago
I'm just hoping the exam doesn't ask about overfitting the dataset to my brain. That would be a whole other problem.
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Yoko
13 days ago
I heard XGBoost is the new hot thing. It's like the avocado toast of machine learning algorithms.
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Nicholle
15 days ago
Decision tree? Are you kidding me? That's just asking for trouble with a small dataset. Definitely not the way to go.
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Merrilee
16 days ago
Hmm, I'm not sure. Maybe logistic regression would be better to avoid overfitting? It's a simpler model, but that might be the safest bet.
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Brandee
27 days ago
I'd go with XGBoost. It's a powerful algorithm that can handle overfitting really well, even with limited data.
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Jaleesa
1 days ago
Random forest is also a good option for preventing overfitting.
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Edelmira
16 days ago
I agree, XGBoost is known for its ability to handle overfitting.
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Gwenn
18 days ago
XGBoost is a great choice for preventing overfitting.
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An
2 months ago
Random forest seems like the way to go here. It's great at handling small datasets and preventing overfitting.
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Lisandra
28 days ago
I agree, Random forest is known for handling small datasets well.
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Shawnda
1 months ago
Random forest is a good choice for preventing overfitting.
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Francine
2 months ago
I personally prefer XGBoost as it has regularization techniques to prevent overfitting.
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Brande
2 months ago
I disagree, I believe Random forest is better because it uses multiple trees to reduce overfitting.
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Kris
2 months ago
I think Decision tree is ideal for preventing overfitting.
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